Research Article
Anomaly Detection in Moving Crowds through Spatiotemporal Autoencoding and Additional Attention
Table 2
Comparisons with the state-of-the-art approaches in benchmarking datasets (frame-level AUC).
| ā | Avenue | Ped1 | Ped2 |
| MPPCA [21] | N/A | 59.0% | 69.3% | MPPC + SFA [22] | N/A | 66.8% | 61.3% | Conv-AE [16] | 80.0% | 75.0% | 85.0% | ConvLSTM-AE [18] | 77.0% | 75.5% | 88.1% | Our approach with one ConvLSTM | 82.9% | 82.3% | 89.3% | Our approach | 85.7% | 85.1% | 92.6% |
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